Testing estimate robustness
The DoWhy library comes with four methods to test the robustness of the estimated causal effect, outlined as follows:
- Random common cause: Adding a randomly generated confounder. If the estimate is robust, the ATE should not change too much.
- Placebo treatment refuter: Replacing treatments with random variables (placebos). If the estimate is robust, the ATE should be close to zero.
- Data subset refuter: Removing a random subset of the data. If the estimator generalizes well, the ATE should not change too much.
- Add unobserved common cause: Adding a unobserved confounder that is associated with both the treatment and the outcome. The estimator assumes some level of unconfoundedness, but adding more should bias the estimates. Depending on the strength of the confounder's effect, it should have an equal impact on the ATE.
We will test robustness with the first two next.
Adding random common cause
This method is the easiest...
